Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MadAppGang/claude-code --skill optimizegit clone --depth 1 https://github.com/MadAppGang/claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/madappgang/claude-code/optimize)<a href="https://agentmods.dev/skills/madappgang/claude-code/optimize"><img src="https://agentmods.dev/badge/skills/madappgang/claude-code/optimize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/madappgang/claude-code/optimize"><img src="https://agentmods.dev/badge/skills/madappgang/claude-code/optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.04941 |
| Opus 5.5 | $0.00023 | $0.01976 |
| Sonnet 5.5 | $0.00011 | $0.00988 |
| Haiku 4.5 | $0.00006 | $0.00494 |
Grade A, and why
optimize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 858 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Skill
Overview
The optimize skill provides comprehensive on-demand performance and optimization analysis for your codebase. It identifies bottlenecks, slow builds, large bundles, inefficient code patterns, and opportunities for performance improvements across all supported technology stacks.
When to Use:
- Performance issues and slow response times
- Large bundle sizes and slow page loads
- Long build and compile times
- High memory usage
- Database query optimization
- API endpoint performance tuning
- CI/CD pipeline optimization
Technology Coverage:
- React/TypeScript/JavaScript (Vite, Webpack, Rollup)
- Go applications (build time, runtime performance)
- Rust projects (compile time, binary size)
- Python codebases (runtime optimization)
- Full-stack applications
- Database queries (SQL, ORM)
Optimization Categories
1. Build Performance
What Gets Analyzed:
- Build duration and bottlenecks
- Dependency resolution time
- TypeScript compilation speed
- Asset processing (images, fonts)
- Code splitting effectiveness
- Cache utilization
Common Issues:
- Unnecessary re-builds of unchanged code
- Large dependency trees
- Inefficient TypeScript configuration
- Missing build caching
- Redundant asset processing
Optimization Targets:
- Reduce build time by 30-50%
- Enable incremental builds
- Optimize dependency resolution
- Improve cache hit rates
2. Bundle Size
What Gets Measured:
- Total bundle size (uncompressed/gzipped)
- Individual chunk sizes
- Duplicate dependencies
- Tree-shaking effectiveness
- Unused code in bundles
- Third-party library sizes
Bundle Analysis:
Bundle Size Breakdown:
├── vendor.js: 847 KB (312 KB gzipped)
│ ├── react-dom: 142 KB
│ ├── lodash: 71 KB (should use lodash-es)
│ ├── moment: 67 KB (consider date-fns)
│ └── ...
├── main.js: 234 KB (89 KB gzipped)
└── [lazy chunks]: 156 KB total
Optimization Goals:
- Keep initial bundle under 200 KB (gzipped)
- Lazy load non-critical code
- Remove duplicate dependencies
- Use lighter alternatives
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 858 lines · 57 tokens per session scan A 4fae6f5b918c
optimize is a skill published in the GitHub repository MadAppGang/claude-code (286 stars, last pushed 7mo ago), licensed MIT. It adds 57 tokens to every session and 4,941 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
performance-optimizer
Analyze and optimize code performance, identify bottlenecks, and suggest improvements.
performance-profiler
Application performance profiling - runtime snapshots, benchmarking, memory analysis, startup timing, and bottleneck detection.
performance
Use when a page or interaction is slow for one user and the fix starts from a measurement — a failing Core Web Vital (LCP, INP, CLS), a heavy JS bundle, wasted re-renders, an over-broad client boundary: profile, attribute the cost to one phase, fix it, re-measure. NOT surviving concurrent load (that is scaling), NOT…
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-engineer
Enforces measure-profile-optimize order, preventing intuition-driven optimization of non-bottlenecks. Now with mathematical depth metrics, optimization ROI calculus, optimization , and verification gates.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.